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Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms' annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm's robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices. [ABSTRACT FROM AUTHOR]

Renewable energy sources play a great role in the sustainability of natural resources and a healthy environment. Among these, solar photovoltaic (PV) systems are becoming more economically viable. However, as the utility of solar energy conversion systems is limited by the availability of sunlight, they need to be integrated with electrical energy storage systems to be more sustainable. This paper aims to improve the control performance of a hybrid energy storage system (HESS) with PV power generation as the primary power source. HESSs stabilize DC microgrid systems by compensating for demand generation mismatches. Batteries and supercapacitors are chosen as energy storage elements; batteries have a high energy density and are capable of supplying and absorbing energy over a long duration, while supercapacitors can store and deliver energy very quickly. To enhance the stability of the system, each storage element is connected to the DC bus using a bidirectional Ćuk converter, which offers high efficiency, a continuous current, and minimal switching losses. This study proposes a proportional–integral (PI) controller combined with the fast nonsingular integral terminal sliding mode control (FNITSMC) for HESSs to adjust the power balance in a DC microgrid. FNITSMC has the advantage of enhancing the system states to reach the equilibrium point of a long sliding surface with a fast convergence rate. The reference current for FNITSMC is obtained using a PI controller combined with a low-pass filter (LPF), which eliminates the peaking current spikes on the battery and diverts them towards the supercapacitor. The effectiveness of the proposed control scheme is validated through the real-time hardware-in-the-loop (HIL) simulations on Typhoon™ HIL-402 with added uncertainties, including load variations at various temperatures and irradiances. [ABSTRACT FROM AUTHOR]

A stable output voltage of a boost converter is vital for the appropriate functioning of connected devices and loads in a DC microgrid. Variations in load demands and source uncertainties can damage equipment and disrupt operations. In this study, a modified twin-delayed deep deterministic policy gradient (TD3) algorithm is proposed to regulate the output voltage of a boost converter in a DC microgrid. TD3 optimizes PI controller gains, which ensure system stability by employing a non-negative, fully connected layer. To achieve optimal gains, multi-deep reinforcement learning agents are trained. The agents utilize the error signal to obtain the desired output voltage. Furthermore, a new reward function used in the TD3 algorithm is introduced. The proposed controller is tested under load variations and input voltage uncertainties. Simulation and experimental results demonstrate that TD3 outperforms PSO, GA, and the conventional PI. TD3 exhibits less steady-state error, reduced overshoots, fast response times, fast recovery times, and a small voltage deviation. These findings confirm TD3's superiority and its potential application in DC microgrid voltage control. It can be used by engineers and researchers to design DC microgrids. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) controllers have already achieved remarkable efficiencies. For smaller photovoltaic (PV) systems, any improvement will not really be worth mentioning as an achievement. However, for large solar farms, even a fractional improvement will eventually create a significant impact. This paper presents an MPPT control scheme using global sliding mode control (GSMC) with adaptive gain scheduling. In the two-loop controller, the first loop determines the maximum power point (MPP) reference using online calculations, while the GSMC with adaptive gain scheduling in the second loop adjusts the boost converter's pulse width modulation (PWM) to force the PV system to operate at the MPP with improved performance. The adaptive gain scheduling regulates the gain of the switching control to maintain the controller performance over a wide range of operating conditions, while GSMC guarantees the system robustness throughout the control process by eliminating the reaching phase and improving MPPT performance. The overall PV system also has Lyapunov stability. Furthermore, the robustness analysis of the proposed controller is also performed under load variations and parametric uncertainties at various temperatures and irradiances. In the simulations, the proposed MPPT control scheme has shown faster response than other controllers, reaching the set point with rise time 0.03 s as compared to 0.07 s and 0.13 s for quasi sliding mode control (QSMC) and conventional sliding mode control (CSMC), respectively. The proposed controller showed an overshoot of 1.2 V around a steady state value of 21.9 V as compared to 1.51 V and 1.45 V, respectively, for QSMC and CSMC for a certain parametric variation. Furthermore, the proposed controller and the QSMC-based scheme showed a steady-state error of 0.3 V, while the CSMC-based approach has a more significant error. In conclusion, the proposed MPPT control scheme has a faster response and low tracking error with minimal oscillations. [ABSTRACT FROM AUTHOR]

In this paper, the Wind Farm Layout Optimization/Expansion (WFLO/E) problem is formulated in a multi-objective optimization way with specific constraints. Furthermore, a new approach is proposed and tested for the variable reduction technique in the WFLO/E problem. To solve this problem, a new method based on the hybridization of the Multi-Objective Evolutionary Algorithm Based on An Enhanced Inverted Generational Distance Metric (MOEA/IGD-NS) and the Two-Archive Algorithm 2 (Two Arch2) is developed. This approach is named (MOEA/IGD-NS/TA2). The performance of the proposed approach is tested against six case studies. For each case study, a set of solutions represented by the Pareto Front (PF) is obtained and analyzed. It can be concluded from the obtained results that the designer/planner has the freedom to select several configurations based on their experience and economic and technical constraints. [ABSTRACT FROM AUTHOR]

A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques. [ABSTRACT FROM AUTHOR]

Solar energy is a promising renewable energy source that can fulfill the world's current and future energy needs. The angle at which a photovoltaic (PV) panel faces the horizon determines the incidence of solar radiation. The incident solar radiation on PV panels could be optimized by adjusting their tilt angles and increasing the power output of the PV array. In this study, solar energy model-based research was conducted in the Saudi Arabian cities of Dhahran and Makkah. This study investigated the performance of a 1 kW monocrystalline silicon PV array in these cities. Analyzing the optimal tilt angle for efficiency and performance improvement of the PV panel is challenging. The optimal tilt angle is determined by combining the data of the Sun's diffuse, direct radiation and the global horizontal Sun radiation. This research examined the four empirical models by applying the electric charged particle optimization (ECPO) algorithm to estimate the solar radiation on sloped surfaces. The model's results were compared to the global horizontal solar radiation based on the daily mean solar radiation value in these cities. The Hay–Davies–Klucher–Reindel model presented the maximum amount of tilted surface solar radiation in the year and at different periods. In contrast, the Badescu model exhibited the weakest results of all the isotropic and anisotropic models. Finally, using the ECPO algorithm, all models indicated that tilted surfaces (IT) received more solar radiation than horizontal surfaces (Ig). [ABSTRACT FROM AUTHOR]

Due to the rapid increase in the consumption of electrical energy and the instability of fossil fuel prices, renewable energy, such as wind power (WP), has become increasingly economically competitive compared to other conventional energy production methods. However, the intermittent nature of wind energy creates certain challenges to the power network operation. The combined economic environmental dispatch (CEED) including WP is one of the most fundamental challenges in power system operation. Within this context, this paper presents a new attempt to solve the probabilistic CEED problem with WP penetration. The optimal WP to be incorporated in the grid is determined in such a way that the system security is within acceptable limits. The system security is described by various fuzzy membership functions in terms of the probability that power balance cannot be met. These membership functions are formulated based on the dispatcher's attitude. This probabilistic and non-convex CEED problem is solved using a new technique combining chaos theory and artificial bee colony (ABC) technique. In this improved version of ABC (IABC), chaotic maps are used to generate initial solutions, and the random numbers involved in the standard ABC are substituted by chaotic sequences. The effectiveness of IABC is tested on two groups of benchmark functions and practical cases. The impacts of dispatcher's attitude and risk level are investigated in the simulation section. [ABSTRACT FROM AUTHOR]

In recent years, wind energy has been widely used as an alternative energy source as it is a clean energy with a low running cost. However, the high penetration of wind power (WP) in power networks has created major challenges due to their intermittency. In this study, an elitist multi-objective evolutionary algorithm called non-dominated sorting particle swarm optimization (NSPSO) is proposed to solve the dynamic economic emission dispatch (DEED) problem with WP. The proposed optimization technique referred to as NSPSO uses the non-dominated sorting principle to rank the non-dominated solutions. A crowding distance calculation is added at the end of all iterations of the algorithm. In this study, WP is represented by a chance-constraint which describes the probability that the power balance cannot be met. The uncertainty of WP is described by the Weibull distribution function. In this study, the chance constraint DEED problem is converted into a deterministic problem. Then, the NSPSO is applied to simultaneously minimize the total generation cost and emission of harmful gases. To proof the performance of the proposed method, the ten-unit and forty-unit systems—including wind farms—are used. Simulation results obtained by the NSPSO method are compared with other optimization techniques that were presented recently in the literature. Moreover, the impact of the penetration ratio of WP is investigated. [ABSTRACT FROM AUTHOR]

Recently, off-grid renewable power generation systems have become good alternatives for providing reliable electricity at a low cost in remote areas. According to the International Renewable Energy Agency, more than half the population of Nigerian rural communities are outside the electricity coverage area. This research examines the potential application of hybrid solar photovoltaic (PV)/hydro/diesel/battery systems to provide off-grid electrification to a typical Nigerian rural village. The performance of four different hybrid systems was evaluated via techno-economic and environmental analysis, and the optimized solution was selected using the HOMER analysis tool. The simulation results revealed that a hybrid PV solar/hydro/diesel with battery storage was the optimized solution and most suitable with the least net present cost (NPC) of $963,431 and a cost of energy (COE) of $0.112/kWh. The results also revealed that the optimal system prevented about 77.1% of CO2 gas emission from being released to the surrounding air as compared with the PV/diesel system (worst case). In addition, the results also showed better performance in technical aspects, making the system appropriate and ideal for rural electrification and clean energy development. Furthermore, the effects of varying some variables such as interest rate, solar radiation, water discharge, capacity shortage, and battery minimum state of charge on the system cost and operational performance were discussed via the sensitivity analysis because these parameters influence the economy and technical aspect of the power system. [ABSTRACT FROM AUTHOR]

This paper studies in detail a systematic approach to offering a combination of conventional and renewable energy that is adaptable enough to operate in grid-connected and off- grid modes to provide power to a remote village located in Nigeria. To this aim, the HOMER pro software tool was used to model two scenarios from the on-and off-grid systems, evaluating in detail the techno-economic effects and operational behavior of the systems and their adverse impacts on the environment. The impacts of varying load demand, grid power and sellback prices, diesel prices, and solar irradiation levels on system performance were discussed. Results showed that, for both cases, the optimum design consists of a diesel generator rated at 12 kW, with a photovoltaic (PV) panel of 54 kW, a 70 battery group (484 kWh nominal capacity battery bank), and a 21 kW converter. The cost of electricity (COE) and net present cost (NPC) were in the range of $0.1/kWh to 0.218 $/kWh and $117,598 to $273,185, respectively, and CO2 emissions ranged between 5963 and 49,393 kg/year in the two configurations. The results of this work provide a general framework for setting up a flexible and reliable system architecture to ensure continuous power supply to consumers under all conditions. [ABSTRACT FROM AUTHOR]

In recent years, the concept of hybrid energy systems (HESs) is drawing more attention for electrification of isolated or energy-deficient areas. When optimally designed, HESs prove to be more reliable and economical than single energy source systems. This study examines the feasibility of a combined dispatch (CD) control strategy for a photovoltaic (PV)/diesel/battery HES by combining the load following (LF) strategy and cycle charging (CC) strategy. HOMER software is used as a tool for optimization analysis by investigating the techno-economic and environmental performance of the proposed system under the LF strategy, CC strategy, and combined dispatch CD strategy. The simulation results reveal that the CD strategy has a net present cost (NPC) and cost of energy (COE) values of $110,191 and $0.21/kWh, which are 20.6% and 4.8% lower than those of systems utilizing the LF and CC strategies, respectively. From an environmental point of view, the CD strategy also offers the best performance, with CO2 emissions of 27,678 kg/year. Moreover, the results show that variations in critical parameters, such as battery minimum state of charge, time step, solar radiation, diesel price, and load growth, exert considerable effects on the performance of the proposed system. [ABSTRACT FROM AUTHOR]